Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143870
Title: Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles
Authors: Le, Nguyen Quoc Khanh
Huynh, Tuan-Tu
Yapp, Edward Kien Yee
Yeh, Hui-Yuan
Keywords: Humanities::General
Issue Date: 2019
Source: Le, N. Q. K., Huynh, T.-T., Yapp, E. K. Y., & Yeh, H.-Y. (2019). Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles. Computer Methods and Programs in Biomedicine, 177, 81–88. doi:10.1016/j.cmpb.2019.05.016
Journal: Computer methods and programs in biomedicine
Abstract: Background and Objectives: Clathrin is an adaptor protein that serves as the principal element of the vesicle-coating complex and is important for the membrane cleavage to dispense the invaginated vesicle from the plasma membrane. The functional loss of clathrins has been tied to a lot of human diseases, i.e., neurodegenerative disorders, cancer, Alzheimer's diseases, and so on. Therefore, creating a precise model to identify its functions is a crucial step towards understanding human diseases and designing drug targets. Methods:We present a deep learning model using a two-dimensional convolutional neural network (CNN) and position-specific scoring matrix (PSSM) profiles to identify clathrin proteins from high throughput sequences. Traditionally, the 2D CNNs take images as an input so we treated the PSSM profile with a 20 × 20 matrix as an image of 20 × 20 pixels. The input PSSM profile was then connected to our 2D CNN in which we set a variety of parameters to improve the performance of the model. Based on the 10-fold cross-validation results, hyper-parameter optimization process was employed to find the best model for our dataset. Finally, an independent dataset was used to assess the predictive ability of the current model.Results:Our model could identify clathrin proteins with sensitivity of 92.2%, specificity of 91.2%, accuracy of 91.8%, and MCC of 0.83 in the independent dataset. Compared to state-of-the-art traditional neural networks, our method achieved a significant improvement in all typical measurement metrics. Conclusions:Throughout the proposed study, we provide an effective tool for investigating clathrin proteins and our achievement could promote the use of deep learning in biomedical research. We also provide source codes and dataset freely at https://www.github.com/khanhlee/deep-clathrin/.
URI: https://hdl.handle.net/10356/143870
ISSN: 0169-2607
DOI: 10.1016/j.cmpb.2019.05.016
Rights: © 2019 Elsevier B.V. All rights reserved. This paper was published in Computer Methods and Programs in Biomedicine and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SoH Journal Articles

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